The blog provides a comprehensive guide to extracting tables from PDF files and images, beginning with a Python tutorial on implementing Optical Character Recognition (OCR) to detect and extract tables into structured formats such as lists, JSON objects, and pandas dataframes. It highlights the use of the Nanonets API for OCR processing and discusses the necessary Python libraries, such as requests, pandas, and numpy, for post-processing the API's response. The blog also introduces a no-code platform for automated tabular extraction and explores free online table extraction tools. Nanonets offers options for creating custom models or using prebuilt ones to detect not only tables but also line items and flat fields from various document types, with ready-made integrations available for popular software and databases. Additionally, the platform supports enterprise-level solutions for OCR and Intelligent Document Processing (IDP), allowing users to automate processes like invoice and accounts payable automation without requiring an in-house development team.